Compressed sensing is a technique that is applied to many areas of signal processing. Compressed sensing involves acquiring and reconstructing a signal by finding solutions to underdetermined linear systems. An important aspect of compressed sensing is the estimation of sparse signals, i.e., signals that can be represented by only a few coefficients in some basis. An algorithm for recovering and estimating sparse signals is Orthogonal Matching Pursuit (OMP). It is based on finding the sparse coefficients sequentially by looking at the residual after all previous coefficients' contributions have been subtracted.